{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the data conversion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This corresponds to the official NILMTK guide [here](https://github.com/nilmtk/nilmtk/blob/master/docs/manual/user_guide/data.ipynb). We try to load the data and just see whether the conversion looks right. To convert the data into the HDF-format look at the [README](../README.md)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilmtk import DataSet\n",
    "from nilmtk.utils import print_dict\n",
    "\n",
    "hipe = DataSet('../test_1week.hdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ul><li><strong>name</strong>: HIPE</li><li><strong>long_name</strong>: An Energy-Status-Data Set from Industrial Production</li><li><strong>creators</strong>: <ul><li>Bischof, Simon</li><li>Trittenbach, Holger</li><li>Vollmer, Michael</li><li>Werle, Dominik</li><li>Blank, Thomas</li><li>Boehm, Klemens</li></ul></li><li><strong>publication_date</strong>: 2018</li><li><strong>institution</strong>: Karlsruhe Institute of Technology (KIT)</li><li><strong>contact</strong>: firstname.lastname@kit.edu</li><li><strong>description</strong>: High-resolution Industrial Production Energy data set</li><li><strong>subject</strong>: None</li><li><strong>number_of_buildings</strong>: 1</li><li><strong>timezone</strong>: Europe/Berlin</li><li><strong>geo_location</strong>: <ul><li><strong>locality</strong>: Karlsruhe</li><li><strong>country</strong>: DE</li><li><strong>latitude</strong>: 49.099398</li><li><strong>longitude</strong>: 8.433545</li></ul></li><li><strong>related_documents</strong>: <ul><li><a href=\"https://www.energystatusdata.kit.edu/hipe.php\">https://www.energystatusdata.kit.edu/hipe.php</a></li><li>Simon Bischof, Holger Trittenbach, Michael Vollmer, Dominik Werle, Thomas Blank, Klemens Böhm: HIPE: An Energy-Status-Data Set from Industrial Production. e-Energy 2018: 599-603. <a href=\"https://doi.org/10.1145/3208903.3210278\">https://doi.org/10.1145/3208903.3210278</a>\n",
       "</li><li>Florian Kalinke, Pawel Bielski, Snigdha Singh, Edouard Fouché, Klemens Böhm: An Evaluation of NILM Approaches on Industrial Energy-Consumption Data. e-Energy 2021: 239-243. <a href=\"https://doi.org/10.1145/3447555.3464863\">https://doi.org/10.1145/3447555.3464863</a>\n",
       "</li></ul></li><li><strong>meter_devices</strong>: <ul><li><strong>HIPE_EEM_MA600</strong>: <ul><li><strong>model</strong>: EEM MA600</li><li><strong>manufacturer</strong>: Phoenix Contact</li><li><strong>manufacturer_url</strong>: <a href=\"https://www.phoenixcontact.com\">https://www.phoenixcontact.com</a></li><li><strong>description</strong>: Energy measurement device to measure electrical parameters in low voltage installations up to 700 V, acquisition of individual harmonic oscillations - can be extended with communication and special functional modules.\n",
       "</li><li><strong>sample_period</strong>: 5</li><li><strong>max_sample_period</strong>: 50</li><li><strong>measurements</strong>: <ul><li>{'physical_quantity': 'power', 'type': 'active', 'upper_limit': 5000, 'lower_limit': 0}</li></ul></li><li><strong>wireless</strong>: False</li></ul></li><li><strong>HIPE_EEM_MA600_apparent</strong>: <ul><li><strong>model</strong>: EEM MA600</li><li><strong>manufacturer</strong>: Phoenix Contact</li><li><strong>manufacturer_url</strong>: <a href=\"https://www.phoenixcontact.com\">https://www.phoenixcontact.com</a></li><li><strong>description</strong>: Energy measurement device to measure electrical parameters in low voltage installations up to 700 V, acquisition of individual harmonic oscillations - can be extended with communication and special functional modules.\n",
       "</li><li><strong>sample_period</strong>: 5</li><li><strong>max_sample_period</strong>: 50</li><li><strong>measurements</strong>: <ul><li>{'physical_quantity': 'power', 'type': 'apparent', 'upper_limit': 5000, 'lower_limit': 0}</li></ul></li><li><strong>wireless</strong>: False</li></ul></li></ul></li></ul>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_dict(hipe.metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ul><li><strong>1</strong>: Building(instance=1, dataset='HIPE')</li></ul>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_dict(hipe.buildings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ul><li><strong>instance</strong>: 1</li><li><strong>original_name</strong>: factory_1</li><li><strong>dataset</strong>: HIPE</li></ul>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_dict(hipe.buildings[1].metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<networkx.classes.digraph.DiGraph at 0x7fc299d96970>,\n",
       " <matplotlib.axes._axes.Axes at 0x7fc299d43ac0>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VAN7e3mzYsAEDAwP++9//0qdPnyzHt2rViqSkJFXvrk6dOmhra8veXgmnEEI+lCRJBeHx48dMmTKFPXv24OXlxdChQ986AUQCr9q1Cb15kzerQeaBQqGcnZtNiSdJek32+CSpgFhaWrJy5UoCAgLYvHkzjRo1Yt++feoOq+hr0wY0Nd/vWB0d5Yo9kpQLmfgkqYA1btyYQ4cOMWPGDEaNGkWXLl24ceOGusMquipXhiZN3nnFnSQNDV5On/7WR1IkSQ51SlIhSklJYfHixcyePZu+ffvi5eVF+fLl1R1W0bR0qXLNzqQk5ZosOVEoEDo6bG3eHO+EBPbv34/R37NDJSk7sscnSYWoXLlyuLu7c+PGDYQQ1KpVi4ULF5KSkqLu0Iqed1hxR3H0KK4HD9KsWTMcHBx4/vy5emKWigXZ45MkNbp+/Trjx48nNDSU+fPn07Vr16JfJ08doqOVy5BduQLPnoGJCdSvr5wFmumxAyEEI0eO5PLly+zduxcDAwO1hSwVXTLxSVIREBgYiLu7O+bm5vj4+NCoUSN1h1RsZWRkMHz4cEJCQgj4u7SUJGUmE19Bi4pS/qZ6+bJyOSYjI+VDuoMHv/MyWVLJlpaWxvLly5k2bRrOzs7MmDEjyzJfUt5lZGQwZMgQHjx4gJ+fH7rvWppKKtFk4isoQUHKumIBAcr3mYuY6ugob9Y7OiqnXtvaqidGqUiKi4tj5syZrF69Gnd3d8aOHYvOexbULc3S09MZNGgQkZGR/Pnnn/JrKKnIxFcQ3mE2Gjo6ymKpOZTCkUqvO3fu4OHhQXBwMHPmzKFPnz7y/t87Sk9PZ8CAATx9+pRdu3ahra2t7pCkIqBYzOp0dHRkzZo1b93P2tqaAwcOFEJEubT9OuklJuae9ACEwCsxkf7ffqs8rpj4+uuv+emnn9QdRon30UcfsX37dtasWcP8+fNp3bo1Z86cUXdYxYqmpiZr167F0NCQzz//nFevXqk7JKkIKDKJz9raGh0dHfT19TE3N2fw4MEkJCQAEBAQwMCBA/O9TSEE1atXp06dOvlzwpCQf5JeJoOAcijXEjQFOqJcTFclPV153Llz+RPHWygUCkJDQ7N85uXlRf/+/fN0/LJly/jxxx8BOHLkCFWqVMn3GKV/2NnZERQUxLBhw+jZsydubm48fPhQ3WEVG2XKlGH9+vVoaWnRu3dv+eiIVHQSH4Cvry8JCQmcP3+eoKAgZsyYUaDtHTt2jKioKO7evUvQ3/W7PsjGjcrhzWx4oKwO/QioiDIZZpGUpLwnKEnZ0NDQYNCgQYSEhPDRRx/RuHFjpkyZovrlUMpd2bJl2bRpEwB9+/YlNTW7eu5SaVGkEt9rlStXxtHRkatXrwKoSoW8tnz5cmrXro2BgQF16tTh/N/VljO7efMm1apVU/1jz86aNWvo1q0bTk5Obwyl2tvb8+OPP9K6dWsMDAzo1KmTqoAlwB9//EHVqlUpX748M2fOhIwMOHv2rcObukA/4Oq/NwgB/v64du2KhYUFRkZGtG3blmvXrql2SUpKYty4cVStWhUjIyPatGlD0t+J9vTp07Rq1QpjY2MaNmzIkSNHco0jN697cQsWLKBixYpYWlqyevVq1fZBgwYxZcoUXr58iaOjIxEREejr66Ovr09ERE7lQqX8oK+vz/Tp07l48SL379+nZs2arFq1ivT0dHWHVuSVK1eOLVu2kJKSQr9+/d5aOFcquYpk4gsLC8Pf35/GjRu/sW3r1q14eXmxdu1anj9/zp9//vnGkk/nz5+nU6dO/Prrr/Tt2zfbNhITE9m2bRtubm64ubmxadOmN4ZANmzYwOrVq4mKiiIlJQVvb29A+dDxiBEj+OOPP4iIiCA2NpZH4eF5urYEYD3w5pUBCgWO2trcvn2bqKgomjRpgpubm2rz+PHjCQ4O5uTJkzx9+pR58+ahoaFBeHg4Xbp0YcqUKTx9+hRvb28+//xzoqOj8xRTdiIjI4mPjyc8PJyVK1cyatQonj17lmUfPT09AgICqFSpEgkJCSQkJFCpUl7KhUofysrKinXr1rFz505WrlyJjY3NB/2yU1poaWmxbds2Xrx4wYABA2TyK6WKVOLr3r07xsbGtGnTBjs7OyZNmvTGPitWrMDDwwNbW1sUCgUff/wxVatWVW0/fvw4Xbt2Zc2aNTg7O+fY1o4dO9DS0qJTp044OzuTlpbGnj17suwzePBgatSogY6ODr179+bixYsAbNu2DWdnZ9q2bYuWlhY//fST8guZy/CJN8raYR+jTH6/Z7dTUhJfaWtjYGCAlpYWXl5eXLp0ifj4eDIyMli1ahW//PILlStXRlNTk1atWqGlpcW6detwcnLCyckJDQ0NOnbsiI2NDf7+/jnG8zZly5Zl6tSplC1bFicnJ/T19QkJCXnv80kFo1mzZpw4cYIffviBQYMG0aNHjzfu30pZaWtrs3PnTmJiYhg8eLDsLZdCRSrx7dq1i7i4OB48eMCSJUuyfe4mLCyMjz76KMdzLFu2jFatWtGuXbtc21qzZg29e/emTJkyaGlp0bNnzzeGOzM/PKyrq6u6nxIREYGVlZVqm56eHuXLZlcm8x/jgTggEmXhzOyuIB344eRJPvroIwwNDbG2tgYgJiaGmJgYkpOTs732Bw8esHXrVoyNjVWvEydO8Pjx42xj0dTUfOMeR2pqKmUzXUP58uWz1I7LfP1S0aJQKOjTpw83b96kWbNmtGjRgnHjxhEXF6fu0IosHR0ddu/eTXh4OEOGDCEjI0PdIUmFqEglvrywsrLizp07OW5ftmwZDx8+ZOzYsTnu8+jRIw4dOsS6deuwsLDAwsKCbdu24e/vn+U+Xk4sLS0JCwtTvU9MTCQ2H26WbwB2R0dz4MAB4uPjuX//PqCcfVqhQgW0tbWzvXYrKysGDBhAXFyc6vXy5Ut++OGHbNv5z3/+ozr3a/fu3cvSc84r+VxZ0aGtrc3EiRO5evUqz58/p2bNmvz2229yOC8Hurq6+Pr6cu/ePYYNGyaTXylS7BLf0KFD8fb2Jjg4GCEEoaGhPHjwQLXdwMCAvXv3cuzYsRx/8P/xxx/UqFGDkJAQLl68yMWLF7l16xZVqlRh48aNb42hV69e+Pn5ceLECVJSUpg6dSoZAG/p9b1NLPAiKYnVq1dz9uxZJmYqqKmhocFXX32Fu7s7ERERpKenc+rUKV69ekX//v3x9fUlMDCQ9PR0kpOTOXLkCI8ePcq2nT59+jBjxgwePXpERkYGBw4cwNfXl169er1zzObm5sTGxhIfH/++ly3lMwsLC5YvX86+ffvYsWMHDRo0IOD1CkJSFnp6euzZs4ebN28ycuRI5HoepUOxS3yurq5MnjyZfv36YWBgQPfu3Xn69GmWfYyNjdm/fz8BAQGq580yW7NmDSNHjlT19l6/vv766zw9KF+3bl1+++03+vXrh6WlJSYmJlSpXPmDr21IuXJUbdiQOXPm0Lp1a7Zs2QIoH7tITk7G29ub+vXrY2tri6mpKRMmTCAjIwMrKyt2797NrFmzMDMzw8rKivnz5+f4G+zUqVNp1aoVbdq0wcTEBA8PD9avX0+9evXeOeZatWrxxRdfUL16dYyNjeWsziKkYcOGHDhwgDlz5jB69GgcHByyzBKWlPT19fH39+fSpUt8++23MvmVAnLJsvzUsyfs2vX2FVuyo1Ao645t3w4ohzdv3LiBn58fvr6+XL58mXbt2uHi4oKTkxOWlpb5HLxUkqWkpLBkyRJmzpyJq6sr06ZNw0wukp5FfHw8HTt2pFWrVixcuFAO45dgMvHlp6AgsLd/Y+WWPNHVVRbdtLHJdnNsbCwBAQH4+fkRGBjIJ598grOzMy4uLjRq1Ej+J5XyJDY2lunTp7NhwwYmTJjAt99+i5aWlrrDKjLi4uLo0KEDdnZ2eHt7y/9XJZRMfPkt81qdeaWr+04LVaempnLixAlVbzAxMZEuXbrg4uJC+/btZQkW6a1CQkIYP348N27cYN68efTo0UP+kP/b06dP+eyzz+jcuTOzZ8+WX5cSSCa+glDI1Rlu3bqFr68vfn5+BAcHY2dnh7OzM126dJHraEq5OnDgAO7u7piamuLj40OTJk3UHVKREBMTQ/v27enatSs//fSTTH4ljEx8BeXcOeXam/7+ygSXeQ3P1/X4nJyU9fhyGN58H3Fxcezduxc/Pz8CAgKoWrUqzs7OODs7Y2Njg4ZGsZvPJBWw9PR0Vq5ciaenJw4ODsycOVOuwANER0fTrl07XF1d8fT0VHc4Uj6Sia+gRUcrK7BfuQLPnoGJCdSvD4MGFXgF9rS0NE6dOqUaEn369KlqSLRDhw7o6+sXaPtS8fL8+XNmzZrF8uXLGT16NOPHjy/1w+ZPnjzB3t6e/v37M3nyZHWHI+UTmfhKkTt37uDn54efnx9nzpyhdevWqt7g+zy8LpVM9+7dY8KECZw+fZpZs2bRr1+/Uj1S8PjxY+zt7fnqq6+YMGGCusOR8oFMfKXU8+fP2bdvH35+fvj7+2NhYaGaJdqsWTM0NTXVHaKkZidOnGDs2LFoaGiwcOFCWrVqpe6Q1CY8PBx7e3u+/vprxo0bp+5wpA8kE59Eeno6Z8+eVQ2JRkZG4ujoiIuLC506dcLQ0FDdIUpqkpGRwfr165k0aRKtWrVi7ty5qjVkS5uwsDDs7e357rvvGD16tLrDkT6ATHzSGx48eKAaEv3rr79o3ry5qjdYvXp1dYcnqcHLly/x9vZm0aJFDBs2jIkTJ5bKX4gePHiAvb0948ePZ9SoUeoOR3pPMvFJuUpISODAgQOqRGhqaqpKgi1btsxSwUEq+cLDw5k0aRL79u1j+vTpfPXVV6VuWPzu3bu0a9eOSZMmMXz4cHWHI70HmfikPMvIyCA4OFg1JPrw4UMcHBxwdnamc+fOmJiYqDtEqZCcO3eOsWPH8vz5c3x8fPjss8/UHVKhCg0NpV27dnh5eTFkyBB1hyO9I5n4pPf26NEj9uzZg5+fH0ePHqVp06aqWaI1a9ZUd3hSARNCsH37djw8PKhXrx7e3t7UqFFD3WEVmlu3btG+fXtmzJjBoEGD1B2O9A5k4pPyRWJiIocOHVINierq6qqGRNu0aZOlyK1UsiQnJ7No0SLmzZtH//79mTp1KqampuoOq1DcvHmT9u3bq65dKh5k4pPynRCCixcvqoZEb9++TadOnXBxccHR0ZHy5curO0SpAERFReHp6cn27duZMmUKI0aMKBW/8Fy7do0OHTqwcOFC+vbtq+5w3i4qSrmoxuXLEB8PRkbQoAEMHlzgi2oUFTLxSQUuMjJSNSR66NAhGjRooOoN1q5dW66DWMJcvXqVcePG8eDBA7y9venSpUuJ/x5fuXKFjh078uuvv+Lq6qrucLIXFKRcRvF1UeLk5H+2vV5G0dFRuYyira16YiwkMvFJhep1dfjXvUFNTU1VEmzbtq0skVNCCCEICAhg3LhxVKlSBR8fH+rXr6/usArUpUuX6Ny5M0uWLKFnz57qDierQl44v8gTkqQmGRkZ4vLly2LWrFmiZcuWwsjISPTs2VOsXr1aPHnyRN3hSfkgJSVF/Prrr6JixYpi2LBhIjIyUt0hFajg4GBRsWJFsXv37nw9r6enp3Bzc8txe506dcThw4ez37hkiRC6ukIoU17eXrq6yuNKqNK7AJ+kdgqFgvr16zNx4kROnjzJ7du36dq1K3v27KFGjRq0bNmSmTNncvnyZYQcmCiWypYtyzfffMPNmzfR09Ojbt26zJ07l+TMw2wlSJMmTfDz82Po0KHs2bPnnY7dsGEDNjY26OvrY2lpiaOjIydOnMjTsdeuXcPe3v7NDUFBb60PqgD0AH2gMuAOpCcmKo87d+6drqG4kIlPKjLMzMwYOHAgW7duJSoqip9++ono6Gh69OhB1apVGTlyJAEBASX2h2ZJZmJigo+PD6dOneLUqVPUqVOHrVu3lshfaGxtbfnzzz8ZPHgwgYGBeTrGx8eHMWPGMGnSJJ48ecLDhw8ZOXIku3fv/rBgZs/OWhItB5eABOAgsAFYDsrjZs9W7ZOWlvZhsRQl6u5yStLbZGRkiOvXr4u5c+eKTz/9VBgaGopu3bqJ5UhkYJkAACAASURBVMuXi4iICHWHJ72HgwcPioYNG4o2bdqIs2fPqjucAvHXX38JMzMzsX///lz3i4uLE3p6emLLli057uPp6SlcXV3FgAEDhL6+vqhTp44ICgpSba9ataqqHU9PT/H555+L3l27Cn0QjUFczGVYExC3M73vBWLU33+vCmLOlCmifv36oly5ciI1NVWEh4eLnj17igoVKghra2vxyy+/qOI4c+aMaNq0qTAwMBAVK1YUY8eOFUIIkZSUJNzc3ISpqakwMjISNjY2ah32lolPKnZiY2PFunXrRN++fYWJiYmwsbERXl5e4ty5cyIjI0Pd4Ul5lJaWJlasWCEsLS3FgAEDRFhYmLpDynfHjh0TFSpUEIcOHcpxn4CAAKGpqSlSU1Nz3MfT01NoaWmJPXv2iLS0NPHDDz+I5s2bq7b/O/GVKVNGbHVzEylaWmI+CGsQKXlIfNdAmINY8TrxKRSioaWlePjwoUhMTBTp6emiSZMmYtq0aeLVq1fizp07olq1amLv3r1CCCFatGgh1q5dK4QQ4sWLF+LUqVNCCCGWLVsmnJ2dxcuXL0VaWpo4d+6ciI+P/+Cv7/uSQ51SsWNqaoqbmxsbN27kyZMnzJ8/nxcvXtCvXz+qVKnCsGHD8PX1JTGX+xqS+mlqajJkyBBCQkKwsrKiYcOGeHl58fLlS3WHlm8+/fRTtm7dSu/evTl27Fi2+8TGxlKhQoW3rnvbpk0bnJyc0NTUZMCAAVy6dCnHfZs2bUovoOyrV7gDycDpXM7dBDABXIChwODXG4TgO2trrKys0NHRISgoiOjoaKZOnUq5cuWoXr06//3vf9m0aROgvKcbGhpKTEwM+vr6tGjRQvV5bGwsoaGhaGpq0rRpU7Uuci4Tn1SslS1bFnt7e7y9vQkJCeHw4cPUqlWLhQsXqmoMLlu2jLCwMHWHKuXAwMCAmTNncv78eUJCQqhZsyZr164lIyND3aHlC3t7ezZt2sTnn3/OX3/99cb28uXLExMT89Z7aBYWFqq/6+rqkpycnOMxVlZWyofTUf6QrwJE5HLu88Az4A4wg6yJwSrT9+HBgwdERERgbGyses2aNYsnT54AsHLlSm7dukWtWrWwtbXFz88PgAEDBtC5c2f69u1LpUqV8PDwIDU1NdfrLVBq62tKUgF79uyZ2LRpk+jfv78oX768aNSokZgyZYo4c+aMSE9PV3d4Ug5OnjwpmjdvLmxsbMSxY8fUHU6+CQwMFGZmZuLkyZNZPn99j2/r1q05Hvvvxxnu3bsnANXw6L+HOps3by6Em5sQINJBWIA4lsd7fJlfVUHs79BB1e7JkyfFxx9//NZrTU9PF1u3bhVaWloiISEhy7Z79+6J2rVrixUrVrz9i1ZAZI9PKrGMjY3p06cPf/zxB5GRkfz666+kpqYyePBgKlWqxFdffcXOnTtJSEhQd6hSJi1btuTkyZOMHTsWNzc3XF1duXv3rrrD+mCdOnXi999/p1u3bpw9e1b1uZGREdOnT2fUqFHs2rWLxMREUlNTCQgIwMPD473aCg4OZoeGBmlaWvwMaAEt3udECgVUq6Z626xZMwwNDZk7dy5JSUmkp6dz9epVgoKCAFi3bh3R0dFoaGhgbGwMKIe0Dx8+zJUrV0hPT8fQ0JCyZcuqtZyVTHxSqVCmTBnatGnDnDlzuHbtGidPnqRRo0YsXbqUSpUq4eDgwOLFi7l//766Q5UADQ0N+vXrx82bN2nYsCG2trZMmDCB+L+H74orJycnVq5ciYuLC8HBwarP3d3d8fHxYcaMGZiZmWFlZcXixYvp3r37e7XTrVs3NsfHY/LqFX8AO4D3WjVVCOjUSfVWU1MTX19fLl68SLVq1ahQoQJDhw5VfV/27t1L3bp10dfXZ/To0WzatAltbW0iIyPp1asXhoaG1K5dGzs7O7Uu6i2XLJNKvefPn7N//378/PzYs2cP5ubmqmXUmjdvXuoKrRZFERERTJkyBX9/f7y8vBg6dGixLoK8c+dOvv76awIDA2nUqFG+ntvLy4vQ0FDWrVsHPXvCrl25L1OWE4UCevSA7dvzNb6iQPb4pFLP0NCQzz//nNWrV/P48WOWL1+OpqYmI0aMwMLCgi+//JKtW7cW+95GcVapUiVWrVpFQEAAmzdvpnHjxuzbt0/dYb23Hj168Ntvv+Hg4MDly5cLrqGJE5Vrb74PHR3l8SWQTHySlImmpiYtWrRgxowZXLp0ieDgYFq0aMHq1auxsrKiQ4cO/Pzzz4SGhqo71FKpcePGHDp0iJ9++olRo0bRpUsXbt68qe6w3kuvXr345Zdf6Ny5M9euXSuYRmxtlQtO6+q+23G6usrjbGwKJi41k0OdkpRHL1++5MCBA6piu8bGxqoh0VatWhXrobfiKCUlhcWLFzN79mz69u2Ll5dXsaz1uH79er7//nsOHjxI7dq1C6aRPFZnEAoFilJQnUEmPkl6DxkZGZw/f15VXun+/fs4ODjg7OyMg4MDJiYm6g6x1IiJicHLy4vNmzczadIkRo0aRbly5dQd1jtZs2YNkyZN4tChQ9SsWbNgGjl3Trn2pr+/8v5d5jU8dXRIS03lgqUltjt2lNie3msy8UlSPggPD1cV2z1y5AhNmjRR9QZr1KhR4guxFgXXr19n/PjxhIaGMn/+fLp27Vqsvu6rVq3C09OTw4cP8/HHHxdcQ9HRygrsV67As2dgYgL16xPXvTvVmzfnypUrVK5cueDaLwJk4pOkfJaUlMShQ4dUQ6La2tqqJNimTZti1xspbgIDA3F3d8fc3BwfH598nzVZkP7v//6PGTNmcOTIEapXr17o7Y8ePRodHR3mzJlT6G0XJpn4JKkACSG4dOmSakj01q1bdOzYERcXFxwdHalQoYK6QyyR0tLSWL58OdOmTcPZ2ZkZM2ZkWfKrKFuyZAnz5s3jyJEjWFtbF2rb9+7dw9bWlvv376Ovr1+obRcmmfgkqRBFRkbi7++Pn58fBw8epH79+qreYJ06dYrV0FxxEBcXx8yZM1m9ejXu7u6MHTsWnfed3l+IFi1axM8//8yRI0f4z3/+U6htu7q68umnn/Ldd98VaruFSSY+SVKT5ORkjh49quoNKhQKVRK0s7NDS0tL3SGWGHfu3MHDw4Pg4GDmzJlDnz59ivwvGQsXLuS3337jyJEjVKlSpdDaPX36NP369eP27dsldvEGmfgkqQgQQnDt2jV8fX3x8/Pj2rVrfPbZZzg7O+Pk5IS5ubm6QywRjh49ytixY9HW1mbhwoU0b95c3SHlat68eaxYsYIjR45QqVKlQmu3devWjB07ll69ehVam4VJJj5JKoKio6MJCAjAz8+P/fv3U7NmTZydnXF2dqZhw4ZFvrdSlGVkZLB27VomT56Mvb09s2fPLvThxHcxa9Ys/vjjDw4fPlxo9yl37NjB/PnzOXXqVKG0V9hk4pOkIi4lJYXjx4+rhkRfvXqlGhJt165dsbhnVRQlJCQwb948fvvtN0aOHMmECROK7ISO6dOns3nzZg4fPkzFihULvL309HRVXcRWrVoVeHuFTSY+SSpGhBCEhISohkQvXryIvb09zs7OdOnSpVCHw0qKsLAwJk6cyOHDh5kxYwYDBw5EQ6Poreb4448/smvXLg4fPlwos4EXL17M4cOH2V4CF6mWiU+SirGnT5+yd+9e/Pz82Lt3L9WrV1f1Bhs3blwkf4AXVWfPnmXs2LEkJSWxcOFC7Ozs1B1SFkIIJk2aREBAAIcOHcLU1LRA23v58iXW1tacPn2ajz76qEDbKmwy8UlSCZGamsrJkydVQ6LPnz+nS5cuuLi48Nlnn6Gnp6fuEIs8IQRbtmxhwoQJNGnShHnz5hXsKirvSAiBh4cHhw4d4sCBAwW+NN6kSZN48eIFv/76a4G2U9hk4pOkEur27duq1WOCgoL49NNPVRNkrKys1B1ekZacnMzChQtZsGABgwYNYsqUKaqK4uomhMDd3Z0TJ06wf//+Ao0rIiKCevXqERoaWuA9zMIkE58klQJxcXHs27cPPz8//P39qVKlimpI1NbWVg6J5iAyMpIff/yRP//8k6lTpzJ8+PAiUYVDCMF3331HUFAQ+/btw9DQsMDaGjRoEDVr1mRiCarNJxOfJJUy6enpnD59WjUkGh0djZOTEy4uLnTs2BEDAwN1h1jkXLp0CXd3dx4/fsyCBQtwdHRUd0gIIRg5ciSXL19m7969BfZ9u3z5Mo6Ojty7d6/ErDMrE58klXL37t1TDYmeOnWKli1bqoZEq1Wrpu7wigwhBL6+vowfP57q1auzYMEC6tatq9aYMjIyGD58OCEhIQQEBBTYfdxOnTrh5ubGwIEDC+T8hU0mPkmSVF68eMH+/fvx8/Njz549mJmZqYZEW7RoUWKXsHoXKSkpLFmyhJkzZ+Lq6sq0adMwMzNTWzwZGRkMGTKE+/fvs2fPHnTftdp6HgQGBvL9999z6dKlErF4QuEnvqgoZS2oy5chPh6MjKBBAxg8GNT4j0eSpKwyMjIICgpSPTMYHh6Oo6Mjzs7OdO7cGSMjI3WHqFaxsbFMmzaNjRs3MmHCBL799lu1ra+anp7O4MGDiYiIwNfXN98XNRBC0KBBA3x8fOjYsWO+nlsdCi/xBQUpq/8GBCjfJyf/s01HB4QAR0eYOBFsbQslJEmS8i4sLEw1JHr8+HFsbW1VvcGiNOW/sN28eZPx48dz8+ZN5s2bR48ePdTSK0pPT2fAgAE8ffqUXbt2oa2tna/nX716NZs3b2bv3r35el51KJzEt3QpjB+vLHWfW3MKhTIJenvDiBEFHpYkSe/n5cuXHDx4UJUIDQ0NVUmwVatWlC1bVt0hFrr9+/fj7u5O+fLl8fHxoUmTJoUeQ1paGv369ePly5fs2LEjX3ugr169olq1auzbt4969erl23nVoeAT3+ukl5iY92N0dd+a/GbNmsXdu3dZsWJFPgQpSdL7ysjI4MKFC6oh0bt379K5c2dcXFxwcHAoUc9/vU1aWhorV67E09MTR0dHZs6cWejLyKWmptK3b19SU1PZtm1bvs7EnDlzJnfu3GHVqlX5dk51KNjEFxQE9vZ5SnqDgA1Aub9fTTU0+HXzZmrlQ1kMLy8vQkNDWbdu3QefS5Kk3EVERLBnzx78/Pw4fPgwjRs3VvUGa9asWSImR7xNfHw8s2fPZvny5YwZM4Zx48YVyKSTnKSkpODq6oqmpiabN2/Otx54bGwsn3zyCdevXy82Fe2zU7BPrc6erRzezCMPIAF4BFTMyGDQsGHZ7peWlpYv4eVVYbcnScVZpUqV+O9//8vu3bt58uQJEyZM4N69e3Ts2JFPPvmEMWPGcPDgQVJSUtQdaoExMjJizpw5nDt3jitXrlCrVi3Wr19PRkZGobRfrlw5tmzZQkpKCv369cu3n2Hly5fniy++YPHixflyPrURuUhNTRXh4eGiZ8+eokKFCsLa2lr88ssvqu3p6eli9uzZonr16sLU1FS4urqK2NhYIYQQ94KCBCB+B2EFojyIGco7fNm+BoKYnOm9Hwg9ECIqSnh6eorPP/9cuLm5CQMDA7F8+XLh6ekp3NzclG3du6ds6/ffhZWVlShfvryYMWOGEEKIgIAAUbZsWVGmTBmhp6cnGjRoIIQQIi4uTnz11VfCwsJCVKpUSUyePFmkpaUJIYRYvXq1aNWqlRgzZowwMTERkydPzu3LJElSHmRkZIiLFy+Kn376STRv3lwYGxsLV1dXsWbNGhEVFaXu8ArU8ePHhY2NjWjWrJn466+/Cq3dpKQk4eDgIPr27StSU1Pz5Zy3b98WZmZmIiEhIV/Opw659vg0NDRwcXGhYcOGhIeHc/DgQX7++WcCAwMBWLRoEbt27eLo0aNERERgYmLCqFGjlAdv2wbACSAEOAhMB27kIRknAOuBxhoaykcfgN27d9OrVy/i4uJwc3PL9rgTJ04QEhLCwYMHmT59Ojdu3MDBwYFJkybRp08fEhISuHTpEgADBw6kTJkyhIaGcuHCBfbt25flfuGZM2eoXr06UVFRTJ48OQ9RS5KUG4VCQcOGDZkyZQqnT5/m5s2bODo6snv3bj755BNat27N7NmzuXr1KqKEPV7cpk0bzpw5wzfffEOfPn3o27cvDx48KPB2tbW12bFjB7GxsQwaNIj09PQPPufHH39M69atWbNmTT5EqCa5ZcXTp08LKyurLJ/NmjVLDBo0SAghRK1atcSBAwdU2yIiIkSZMmVEamqquNetmwBEWKZenC2Ijbn0+LRAGIEwB+ECIhSEGDBAeHp6ik8//TRLHNn1+MLCwlTbbW1txcaNG9/YVwghIiMjRbly5URiYqLqsw0bNgh7e3shhLLH9+/rliSp4CQnJ4vAwEDx7bffCmtra2FtbS2++eYbsXfvXpGcnKzu8PJVQkKC8PLyEqampmLixIkiPj6+wNt8+fKlaNeunRg4cKBIT0//4PMdP35cfPzxx6pRsuIm1x7fgwcPiIiIwNjYWPWaNWsWT548UW3v0aOHalvt2rXR1NRUbn/xAoDMtz91UfbmcjIeiAMigT+BjwD/DRtYtGgRN27coFevXnz77bfMnDmT8+fPEx4eTnBwMJGRkcq2Mt1s1dXVJSEh+9YePHhAamoqlpaWqtiHDx9OVFSUah+5er0kFR4tLS06derEokWLuHv3Ln5+flSuXJmffvqJihUr0rNnT1atWqX6v16c6enp4enpyeXLlwkPD6dmzZosX748X3pjOdHV1cXX15d79+4xbNiwD77X2Lp1a0xNTfH19c2nCAtXrsuMW1lZUa1aNW7fvp3j9lWrVtG6des3tt3PpwVTO/TqhZuZGSEhIfTu3ZvIyEgiIyMJCwsjKiqK//73vzx69AhQJj5LS0ssLCy4ceMGW7duJT4+nitXrhAfH8+1a9ewsLCgSpUqaGlpERMTk+NK66Vh5pkkFUUKhYK6detSt25dfvjhB2JiYggICMDPz49x48ZRo0YN1SzRhg0bFtv/q5UrV2bNmjWcO3eOsWPHsnjxYnx8fPjss88KpD09PT327NmDg4MDI0aMYOnSpe9dlUOhUDBu3DgWLFhA9+7d8znSgpfr4wzp6ek0a9aM3r17891331GuXDlu3LhBUlIStra2LFy4kN27d7NmzRqqVq1KdHQ0J0+epFu3btz/4QeqzZ1LKv9kV3ugPzA0m7YGAVWAGZk/1NGBadPwevnyjccRMj+icP/+fapVq0Z4eDgxMTFERkYyevRo6tati5WVFSdOnODWrVtYWlry5MkTEhMT0dDQwNDQkCZNmqgSYdmyZfn0008JDg4mMDCQY8eOFeoUZEmScpeSksKJEydUlSWSkpJUSbB9+/b5vlRXYRFCsH37djw8PKhXrx7e3t7UqFGjQNp68eIFnTt3pnHjxixevPi9f3FIS0vjk08+YfPmzTRr1iyfoyxYuaZ7TU1NfH19uXjxItWqVaNChQoMHTqU+Ph4AEaPHk3Xrl3p1KkTBgYGtGjRgjNnzigPzofn7xACBg3K8+4VK1akQYMGdOrUCXNzcxwcHFi4cCF79+6lfv36PHnyhGrVqhEfH8/Zs2dp3bo1QUFBrFu3ju3bt3P16lX++OMPNm3axNWrVzE1NcXQ0JAaNWrQtm1b1S8As2bNYtWqVfj7+3PhwgUeP34sH3mQpEJQrlw52rdvj4+PD7du3eLgwYN8/PHHzJ8/H3Nzc7p27cr//d//ERERoe5Q34lCoaBXr15cv36dNm3a0KpVK8aMGcPTp0/zvS0DAwMCAgI4d+4cY8aMee+JRGXKlGH06NEsWLAgnyMseAX7AHvPnrBrV+7LlOVEoYAePWD79vyPK4+EEDx//pzIyEgeP36sGmbN7hUbG4upqSkWFhZvfRkbGxfb4RlJKqqePn1KYGAgfn5+7N27F2tra1VvsEmTJsWq2G5UVBRTp05lx44dTJkyhREjRuT7MnBxcXF06NABOzs7vL293+tn0osXL6hWrRrnzp3D2to6X+MrSEVm5ZY36OrC0aNgY5PvYRWEtLQ01TDr217JycmYm5tnmxRf36O0sLDA3Ny82A7dSJI6paWlcfLkSdWQaFxcHF26dMHFxYUOHToUWN26/HblyhXGjRvHw4cP8fb2pkuXLvn6S/PTp0/57LPP6NSpE3PmzHmvc3t4eJCSksLPP/+cb3EVtGK7VmdxlpSUlKcEGRkZiY6OTp56kWZmZrJWmiTlIDQ0VLWg9tmzZ2nTpo2q2O5//vMfdYeXKyEE/v7+jBs3DisrK3x8fKhfv36+nT82Npb27dvj7OzMjBkz3jn5PXr0iAYNGnD37l2MjY3zLa6CJKszFGFCCOLi4t6aHB8/fsyzZ88oX758tj3Hf78MDQ3lUKtUasXHx7Nv3z78/Pzw9/enUqVKqiFRW1vbIvsLZGpqKsuWLeOnn36iR48eqkc98kN0dDTt2rWjV69eeHl5vfPx/fv3p0GDBnh4eORLPAWt8OrxnTunXLvT31+Z4DKv4fm6Hp+Tk7IeXzEZ3ixKUlNTiY6OzlMvMjU1NU+9SHNz83yv6SVJRUl6ejpnzpxRVZaIiorCyckJZ2dnOnbsiKGhobpDfMOzZ8+YPn06f/zxB99//z2jR4/Ol/+nT548wd7eHjc3N6ZMmfJOx164cAEXFxfu3r2br9UgCkrhV2CPjlYuQ3blCjx7BiYmUL++cvamrMBeKF6+fMmTJ0+y7Tlmfv/kyRP09fXzlCQrVKhQrCYPSFJ27t+/rxoSPXnyJC1atFANiVavXl3d4WVx69YtPDw8uHz5MnPnzqVXr14fPJLz+PFj7O3tGTx4MD/88MM7Hdu+fXu++uor+vfv/0ExFIbCT3xSsZGRkcGzZ8/y1IuMi4vDzMzsrQnS0tISfX19OdQqFXkJCQns378fPz8/9uzZQ/ny5VVDoi1atMhx8YvCdujQIdzd3TEwMGDhwoXYfOCIWXh4OPb29gwfPpzx48fn+bg9e/YwZcoUzp8/X+T/f8vEJ+WL1NRUoqKicuw9Zv4cyPNQa3EYNpFKvoyMDM6dO6caEg0LC8PBwQEXFxc6d+6s9kkd6enp/P777/z444906NCBWbNmUaVKlfc+X1hYGPb29nz77beMGTMmT8dkZGRQt25dfvvtN9q3b//ebRcGmfikQpeQkJCnXmRUVBQGBgZvfezDwsICU1NTOdQqFZqwsDBVsd1jx45hY2Oj6g1+8sknaovrxYsXzJkzh2XLlvHtt9/y/fffv/ejGw8ePMDe3p7x48f/U3XnLVasWMHOnTvZs2fPe7VZWGTik4qsjIwMnj59+tbFAyIjI3nx4gUVK1bMU09SX19f3ZcmlSCJiYkcPHhQdW9QX19flQRbt26d7w+e58WDBw/44YcfOH78OLNmzaJ///7v9YvhvXv3sLe3Z+LEiXz99ddv3T85ORlra2sOHz5M7dq13yf0QiETn1QivHr1KstQa07DrJGRkWhoaOTae3z9qlixolp+aEnFlxCCCxcuqB6cv3PnDp07d8bZ2RkHBwfKly9fqPGcOnWKsWPHkp6ejo+PD59++uk7n+POnTu0a9eOqVOnMnRodistZzV9+nTCwsJYvnz5+4RcKGTik0oVIQQvXrzI01BrdHQ0xsbGeepFmpqaFvkb+lLhe/z4sWpI9PDhwzRs2FDVG6xVq1ah/JvJyMhg06ZN/PDDDzRv3py5c+e+8wzV27dv065dO2bMmMGgt6yfHB0dTY0aNbh58ybm5uYfEHnBkYlPknKQnp5ObGxsrr3H16+XL19ibm6e68IBr1+y4kfplJSUxJEjR1S9wbJly6qSYNu2bQt8IldiYiI+Pj4sXLiQoUOHMmnSJIyMjPJ8/M2bN/nss8+YO3fuWx9Z+PrrrzE3N2fatGkfGnaBkIlPkvJBcnJyts9GZpcwy5Url6deZMWKFYvMlHkpfwkhuHLlimqW6I0bN+jYsSPOzs44OTlhVoDPNEdERDBlyhT8/f3x8vJi6NChef53dv36dTp06MCCBQv44osvctwvJCSEtm3bcv/+/SK53rBMfJJUiDJX/Hjbox+y4kfpERUVhb+/P35+fhw4cIA6deqoeoP16tUrkO/t+fPncXd3JzY2lgULFtCpU6c8HXf16lU6duzIL7/8Qu/evXPcr2vXrnTp0oXhw4fnV8j5RiY+SSqiZMWP0unVq1ccO3YMX19ffH19ycjIUCVBe3v7fF1GUAjBrl27+P7776lZsyYLFiygVq1abz3u0qVLdOrUiaVLl9KzZ89s9zl69CjDhg3jxo0bRe5RI5n4JKkESEpK4smTJ3l69ENW/Cg+hBDcuHFDNSR6+fJlVSWFLl26YGFhkS/tvHr1isWLFzNnzhz69u2Ll5fXW2egnj9/HkdHR/7v//6Pbt26ZRu7ra0tnp6euLi45Euc+UUmPkkqRT6k4kduj37Iih+FIyYmhr179+Ln50dgYCCffPKJqjfYqFGjD/4eREdH4+XlxZYtW5g0aRKjRo3KddLNuXPncHJyYtWqVTg7O7+xfePGjfzvf//jyJEjHxRXfpOJT5KkbMmKH0VbamoqJ06cUM0STUxMVBXbbd++/QfNHr5+/Trjx48nNDSU+fPn07Vr1xyT6pkzZ3BxcWHt2rU4ODi8EeNHH33Ezp07adq06XvHk99k4pMk6YO9S8UPPT29XHuPsuLH+7l165ZqSDQ4OBg7OztVZYnKlSu/1zkDAwNxd3fHwsICHx8fGjZsmO1+J0+epHv37mzYsIEOHTpk2bZgwQKCg4PZsGHDe8VQEGTikySp0MiKH4Xj2bNnBAYG4ufnR0BAAFWrVlUNiTZt2vSdfqFIS0tj+fLlTJs2TVWlPbt7i8ePH6dnz55s3rw5yyLV8fHxVK9enQsXLhSZavcy8UmSVCT9u+JHTo9+yIofuUtLS+PUqVOq3uCzb0teiwAAIABJREFUZ8/o0qULzs7OdOjQIc9r18bFxTFz5kxWr16Nu7s7Y8eOfWOG8JEjR3B1dWXbtm3Y2dmpPh83bhwKhQJvb+98vbb3JROfJEnFXn5U/Pj38GtJrfhx584d1YLaZ86coXXr1qoh0apVq+bpeA8PD4KDg5kzZw59+vTJ0ts+ePAgffv2ZefOnbRp0waAhw8f0rhxY+7du1ckqtrLxCdJUqkhK35k9fz5c/bt24efnx/+/v5YWFiohkSbNWuW6+MsR48eZezYsWhra7Nw4UKaN2+u2rZv3z769+/P7t27admyJQBffPEFtra2uPfvD7//DpcvQ3w8GBlBgwYweDAU4Io1mcnEJ0mSlI0PqfiR0+SdolzxIz09nbNnz6qGRCMjI3FycsLZ2ZlOnTpl21PLyMhg7dq1TJ48GXt7e2bPnq26j+fv78+gQYPw8/OjWbNmXF+zhocjRtBZCBQAycn/nEhHB4QAR0eYOBFsbQv0WmXikyRJ+gAlteLHgwcPVEOif/31F82bN1f1Bv9d3SEhIYF58+bx22+/MXLkSCZMmIC+vj6+vr4MGTKEoCFDqLpoEemJieS6JIJCoUyC3t4wYkSBXZtMfJIkSYUkt4of/56887riR14e/Sioih/r169nzZo17NixgwMHDqgSoampqSoJtmzZUrXIdVhYGBMnTuTw4cPMmDGDgQMHcnnkSGr873+8U4S6ugWa/GTikyRJKoIKq+LHiRMn8PDw4Nq1a2hqalK7dm1+/vlnbLMZblQoFISEhBAfH696cP7hw4c4ODioiu0aGxtz5swZxo4dS/XYWNY8eIDmq1fv/gXQ1YWjR8HGJtvN1tbWrFix4o3nBvNCJj5JkqRi7EMqfpiamrJz505cXV1VFeIfPXpErVq1aN269RtDrQqFgtu3b/Pxxx+rPnv06JGq2O7Ro0dp2rSpapao3oABWAYF5T68mROFAnr0gO3bs938IYlPFvuSJEkqxhQKBUZGRhgZGVGzZs1c9/13xY9Tp06hUCgoX748vr6+b1T80NPTIyUlhfbt23P27FkA6tati4aGBpMnT6Z///4EBwezdOlS7t+/T506dejZsyfXrl2jt709Z548yZL0FMBvwEIgEhgDDAL6A9cAB2AdUA5ACPx8fZlSrx73Hz36//buO67K8n3g+AcX4mCK4MBF7l1q4gINUBnmKMuF42uWP60ELM1ypKalgmV9c5UrRb+apslSHCCmEpqkpOJWyIEDUTYcrt8fR0+SqKjAYdzv1+u8lGdeD+Nc576f+7lumjVrxpIlS2jVqhXDhg3j8uXLuLu7U7ZsWaZNm8bHH3/MoUOH8PLy4sSJE9y5c+fx3whRFEVRSqXExEQxNzcXDw8PCQwMlNu3b+vWpaSkyPz586VVq1ayZcsW+f777wWQt956S/r06SMdOnQQa2trAaRy5crSqFEjady4sVSqVEnGjRsnu3v1koxy5US04zVFQABxB0kEiQapANID5BzIHZCmIKvub3sExBLk0PjxkpWVJatWrZK6detKWlqaiIjUrVtXQkJCdPHGxcWJubm5BAQEiEajeeJ1l7ynMxVFUZQ8MTY2Zv/+/RgYGPDOO+9gaWlJnz59uH79OkZGRlSrVo2qVavSr18/xt4faDJ79my2bdtGREQEffv25dNPPyU2NpatW7eyePFiTExMEBFML1+mfFbWI+ecBBgDzYEWgDPQADABegNH72+3HHgXeDUxkbJlyzJ8+HAMDQ05dOhQrteydu1aXFxccHFxeWrhAZX4FEVRSrGmTZuyatUq4uLiiI6O5sqVK0yYMOGx22dlZXH16lWOHj3K4cOHmT9/PjVr1qRNmzb07NmT69evs2XLFq7HxOS6v9VD/zfK5euk+/+/BPgApuvXY2pqiqmpKbGxsVy5ciXX4166dIlNmzbptn0SdY9PURSllBMREhMTAejcuTO//PILX3/9NSEhIZw5c4aePXty7do1AFq0aKGbp/H27ds0b94cZ2fnHKNHTUxMMH3/ffjtt+eOyQb4FPh00CBYs+aR9f8eeGNjY8OwYcNYvnz5U4+tEp+iKEoJlZaW9sRHIS5cuEBsbCzJyclUrFgRCwsLbt26hYWFBRcuXMDIyAhjY2MmTJiAtbU1PXv2ZPXq1fTu3RvQTkTbr18/+vXrR82aNdmyZQs+Pj6cOnWKBdWrM6pcuVy7O/PiHaCfgQGOJiZ0ECElJYXQ0FC6detG1apVsbKy4vz587rthw4dSvv27dmxYweOjo5PLLemEp+iKEoxotFocozMfNIrJSVF9xD8w6/WrVvTs2dPypYty9KlSzl69CiJiYmkp6fz9ttvM3/+fIyNjVm1ahXXrl3TJbqZM2cyatQoUlNTWbJkCQ0aNKBr1650796dtLQ0DA0Nad26NSdOnCDxzBmyn+NRgwfaAcvLlWP8/v2cMTPDyMiILl260K1bNwA++eQT3n//fT7++GM+++wzJk6cyLZt2/j4448ZNGgQt2/ffuyx1XN8iqIoeib/Knv2pCLaN2/exMzMLE8PrJuZmeVr2bOkpCRCQkLw9/cnICAACwsLXQWXjh07Uq5cOa5evcpnn31GQEAAB6ytqX/sGAbPk2ae8hzfi1CJT1EUpYCkp6fnqfrKtWvXKFu2bJ4m3bW0tCzUQteXLl3SFa4+cOAAHTt2xM3NDVdXV2xtbXXbpaam4uvry8KFCxk1ahSffvoplU+cIKtrVypqNM9+4qdUbnkRKvEpiqI8g+zs7DzX20xKSqJ69epPrbVpZWVVZKY20mg0RERE6EqSXb9+HRcXF9zd3XFycnpklgYRYcOGDUyePJn27dvz1VdfYWtrS1ZWFkOHDqXz8eOMv3gRg5SUvAehanUqiqIUvGeZYcHY2DjPMywUh8ls7969y44dO3Tz8tWsWVPXhdm+ffvHDhQ5dOgQnp6eZGRksHDhQt39N41Gw/Dhw4mPj2fbtm0YrVpF1oQJlMnIePIzdGp2BkVRlBeTkZHx1Dn1Hryys7NzbZn9e1n16tWpUKGCvi/thZ07d07XhRkREUGXLl1wd3fH1dX1qTOxX758mcmTJ7Nv3z6++OILhg0bpkvwGo2GUaNGERcXx/bt26lUqRIiwtsvvcSimjWxOnxYm+BSU/854IP5+FxctPPxFUD35sPUqE5FUYoVEeH27dt5GgiSmJiY6yzqjRs3xt7e/pFZ1PU5/11By8rK4sCBA7ouzDt37uDq6sr777/P1q1b89TVmpSUxJdffsnixYsZP348y5cvp3Llyrr12dnZjBkzhkuXLhEQEKCbLmnHjh2cqlKF6vv2wc2b2hnYjx+HhAQwM4OWLWHECDUDu6IopUtycnKeWmbXr1+nSpUqTx0EYm1tjYWFRbHoaiwoCQkJBAcH4+/vT3BwMPXq1dPNnPDKK6/k+Xuj0WhYvXo1U6dOpUePHsyZMwcbG5sc22RnZzN27FhOnDhBUFBQjkTq5OTEsGHD8PDwyNfre14q8SmKUmCysrIe29X475ZaVlZWniZdtbKywtDQUN+XViSJCKdPn9Z1Yf7xxx/Y29vrujBr1ar1zMcMDQ3F09OTSpUqsXDhQjp06JDrecePH8/Ro0fZsWMHVatW1a37888/cXFx4cKFC0Wmi1glPkVRnomIkJCQkKfWWUJCAtWqVcvTQBBjY+MS3dVYUDIzMwkPD9d1YaampuoGpnTv3v25Z2c/e/YsH330EVFRUXz11Ve8+eabuf58RIQJEyZw6NAhdu7ciYmJSY71Hh4eNGvWjMmTJz9XHAVBJT5FUQDtc1h5SWbXrl3DyMjoqYNArK2tqVat2hNLRynP59atWwQGBuLv78/OnTtp1KiRrguzTZs2L/QB4s6dO8yaNYvVq1czceJEJkyYQMWKFXPdVkTw9vZm37597Nq165Hi0H///TctW7bk3LlzmJmZPXdM+U0NblGUEkyj0XDjxo2nDgK5du0a6enpubbE2rZt+0hXo5GRkb4vrVQREU6cOKFr1R0/fpwePXrg7u7ON998g7W19QufIysri6VLlzJz5kxef/11/vrrL6ysrB67vYgwefJk9u7dy+7du3OdEeHbb79l6NChRSrpgWrxKUqx86CSfl5aZrdu3cLc3DxPA0FMTExUV2MRkp6eTlhYGP7+/vj7+6PRaHB3d8fNzQ0HB4fHtsKeR1BQEN7e3tSsWRNfX19atWr1xO1FhM8++wx/f3/27NmDhYXFI9skJSVRr149fv/9dxo0aJBvseYH1eJTlCIiLS3tseWt/t1aq1ChQq5di40bN87xtaWlJeXKqT/z4iI+Pp6AgAD8/f3ZtWsXzZs3x93dnW3bttGiRYt8/2Dy119/4e3tzYULF/Dx8cHV1TVP5/j888/Ztm0be/fuzTXpAaxYsQIHB4cil/RAtfgUpUDlRyX93EY1PvzslFJ8iQjHjh3TdWGeOnUKJycn3N3d6d27N5YF9FzbjRs3mDZtGps3b+azzz5j7Nixea7/OWvWLNavX8/evXsf2xWq0Who2LAh69atw87OLj9Dzxfqo6CiPKN/V9J/WnkrU1PTR7oWbWxsaN++fYFW0leKprS0NPbs2aPrwixfvjzu7u7Mnj2bbt26FeiQ//T0dBYtWsS8efMYMmQIp06dwtzcPM/7f/nll6xdu5bQ0NAn3v/75ZdfsLa2LpJJD1TiUxSdjIwMrl+//tRBIE+qpN+5c+dHylsVZiV9pWi6cuWKrgtz7969tGnTBnd3d3bs2EGTJk0K/AOPiLBlyxY+/vhjmjdvzv79+2ncuPEzHWPBggX8+OOPhIaGUqNGjSdu6+Pjw0cfffQiIRco1dWplGhPq6T/8OvevXu5lrf69yCQolRJXymaRIQ//vhD14V5/vx5evbsibu7O7169XqmVtaLOnLkCF5eXiQkJODr64vjc0wO+/XXX/Ptt98SFhZG7dq1n7jtgQMH8PDwICYmpsg+yqISn1IsJSUl5WkQSHx8PMbGxk+tBlKcKukrRVNKSgq7du3STdJatWpV3bN1nTt3LvSW/5UrV5gyZQo7duzQzZz+PInou+++w8fHh7CwMOrUqfPU7QcMGED37t0ZP37884RdKFTiU4qMzMzMPE/a+bhK+v9+Va9eXZW3UgpMbGys7l5deHg47dq10z1y0LBhQ73ElJKSwoIFC/jmm28YM2YMn3zyySNz6OXVkiVLmDt3LmFhYdSrV++p2587d46OHTty8eLFIj0AS93jUwrUvyvpP+l1584dLC0tH+leLI2V9JWiKTs7m8jISF0XZlxcHL1798bDw4N169bl+hB3Ycbm5+fHlClTsLOz4/Dhw9SvX/+5j/fDDz8wZ84c9u7dm6ekB9ou0XfeeadIJz1QLT7lOSUnJ+dpIEheKuk/eFlYWBTZewJK6XXv3j1CQkJ0XZiWlpa6Lkw7O7si8Tv722+/4enpCcDChQvp3LnzCx1v5cqVTJ06lb179+a55Xr79m1eeukloqOjqVmz5gudv6CpxKfoPKmS/r9fmZmZeaoGUr169XytMKEoheHixYu6Vt2BAwews7PTzXBQlB7IvnjxIpMmTeLAgQPMnTuXwYMHv/B96p9++onJkyezZ8+eZxr5OXfuXGJiYli1atULnb8wqMRXwokId+7cydNAEFVJXymtNBoNhw4d0iW7+Ph4XF1dcXNzw9nZOcc0O0XB3bt3mTt3LsuWLePDDz9k4sSJzz0Lw8PWr1+Pt7c3u3btolmzZnneLyMjg/r16xMUFPTUcmdFQeHf44uP186+e+wYJCaCiQm0agUjRxba7LslwYtW0re2tqZp06aPlLcqCt02ilIYEhMT2bFjB/7+/gQFBVGzZk3c3d354Ycf6NChQ5Ec4avRaFixYgXTpk2jZ8+eHDt27Lnm2MvNxo0b8fLyIiQk5JmSHmgTZvPmzYtF0oPCbPFFRsLcuRAUpP06Le2fdUZGIAK9e8Mnn0D79oUSUlHzcCX9J72uXr1KWlraU6eEUZX0FSWns2fP6iZpjYyMpEuXLrouzLwM1den3bt34+XlhYmJCQsXLuSVV17Jt2Nv3ryZcePGsWPHDlq3bv1M+4oIrVu3Zv78+fTs2TPfYipIhZP4Fi+GiRMhNVWb4B4bjYE2CS5YAGPHFnhYhUFEuHv3bp6qgeSlkv6Dl6mpqepqVJSnyMrK4rffftN1YSYmJuoGpjg6Ohb50YcAp0+fZuLEifz111/MmzeP/v375+vf/rZt2xgzZgzBwcG0bdv2mfcPCQnBy8uLY8eOFZv3pKd2dc6YMYOzZ8+ydu3a5zpB7+bNefvsWYZnZDx9YxFISdEmSSjSye9JlfT//SpfvnyuyatRo0Y5Wmqqkr6ivLiEhASCgoLw9/cnODiY+vXr4+7uztq1a3n55ZeLZBdmbm7fvs3MmTNZu3YtkyZNYtOmTfn+TKq/vz/vvPMOgYGBz5X0QFuezMvLq9gkPSjoe3yRkQRdvAh5SXoPe5D82reHdu0eu9mdO3f45JNP+OWXX7h79y62trZ4eXkxcuTI5wpXo9HkWt4qt9ZacnJyrsmsZcuWODk5qUr6ilJIRISYmBhdq+7o0aM4ODjg7u7O/Pnz8+0eWGHJzMxk8eLFzJ49mwEDBnDixAmqV6+e7+cJDg5m1KhRbN++nXZPeJ99kujoaI4dO8a2bdvyObqCVWCJT0SQOXMok5r6fAdITdXeE9y8OdfVGRkZODo6Ur16dQ4ePEjt2rXZvXs3w4cPJyEhAS8vL10cL1JJ39ramtq1a9OuXbtHylsVp084ilKSZGRkEB4erkt26enpuLm58fHHH9OjR49ieV9bRAgICGDixInUrVuXPXv20KJFiwI5V0hICB4eHmzdupVXX331uY/j6+vLuHHjil91JHkCR0dHGTdunAwZMkS37ODBg2JnZycmJibSqlUr2bt3r26dvb29TJkyRTp16iQVK1aUMxUqiD3Icm0npqwE6QziDWIKUg8k8P46ATkP0hWkCshrIP9XtqwMGTAg19iWLFkiFhYWEhYWJr/++qssW7ZMZs6cKU5OTlK2bFlp3769mJmZSZkyZaRSpUpia2srnTt3FltbW2nVqpXMnj1bFi1aJE5OTmJhYSFWVlYyefJkycrKEhGRlStXSufOncXb21tMTU2lXr16EhgY+KRvl6IoBejGjRuyevVqefPNN8XU1FQ6dOggs2bNkqNHj0p2dra+w3shx44dE0dHR2nSpIkEBAQU6PXs3r1bqlWrJuHh4S90nKtXr4qZmZncvHkznyIrPE9MfGFhYVKlShVd4ouLixNzc3MJCAgQjUYjO3fuFHNzc4mPjxcRbeKzsbGR6OhoyZwzRzIMDR9JfOVAloFkgXwPUgMk+/76jveTYjpIOEhVEMeaNcXLy0sGDx4sPXr0kGbNmom5ubkYGBiIkZGRvPLKK+Lq6ir/+c9/5NNPP5Wvv/5aypQpIwsWLJDQ0FAxMjKSxMREERHJysoSa2trOXjwoIiIvP766zJmzBhJSkqS69evS/v27WXJkiUiok185cqVk2XLlklWVpZ8//33UqNGjWL/B6YoxUV2drYcP35c5s6dK506dRJjY2Pp16+f/Pjjj3L16lV9h5cvrl27JmPGjJHq1avLt99+KxkZGQV6vtDQUKlWrZqEhoa+8LE+/fRT+b//+798iKrwPTHxiYgMGjRIl/i+/PJLGTp0aI71zs7OsmrVKhHRJr6pU6dqVwwZIgKPJD7bh1p4ySCAXAW5BFL2/rIH64eA9DI2lnnz5smaNWskJCREjh8/Ljdu3JDXXntNJk2alGvMVlZWsnbtWhER6dy5s6xevVpERHbu3CkNGjQQEe0vXIUKFSQlJUW3n5+fnzg4OIiINvHZ2trq1iUnJ2tjLSF/cIpSFKWlpUlwcLCMHz9e6tWrJ3Xr1pXx48dLcHCwpKWl6Tu8fJOamipz584VCwsL8fT0lNu3bxf4OcPDw6VatWqya9euFz5WUlKSWFpaypkzZ/IhssL31Ht8devWJTY2FoBLly6xadMmtm/frlufmZlJ9+7ddV/b2Nho/5OYmOvxrB/6/4M6A0nATcD8oWUANgBVq+Y6oWG1atW4evXqI8uzsrK4efMm1apVA2Dw4MGsX78eDw8P/Pz8GDx4sO5aMjMzc0yomJ2d/U/8gLX1P9E+qIqQlJSU63UpivJ8rl+/rpukdffu3bRo0QJ3d3e2b99O8+bNS9S9dBFh06ZNTJo0idatW3Pw4MFCmcXh4MGD9O/fn3Xr1vHaa6+98PFWr15N586deemll/IhusL31MR3+fJl3S+ejY0Nw4YNY/ny5Y/dXvdLamLyTIHUAG4DKfyT/GIBKlTIdXtHR0emTJlCcnJyjlGTmzdvxtDQkI4dOwLw5ptv4u3tTVxcHL/88gsHDx7UXYuhoSE3b95UjxAoSiESEf7880/dwJSYmBicnZ3p27cvS5cuxbKEVnCKjIzE09OTpKQkVqxYkaPBUJB+//13Xn/9dVavXo2zs/MLH0+j0bBw4UJWrlyZD9HpxxMfaNm/f3+O1t3QoUPZvn07O3bsQKPRkJaWRmhoKHFxcY/u3KoVPENx4rpAO2AGkAEcBLYDPGaaj2HDhlG7dm3efPNNLl68SGZmJjt27OCDDz5gxowZmNxPvJaWljg4ODBy5Ejq169P06ZNAahRowbOzs54e3tz9+5dsrOzOXfuHGFhYXmOWVGUvElNTSUgIICxY8dSp04d3njjDW7dusWcOXOIj49n48aNeHh4lMikFxcXx7Bhw3j99dcZOXIkR44cKbSkd+TIEdzd3VmxYgW9e/fOl2Nu374dc3PzF54BQp+emPg+//xzPDw8dF/b2Niwbds25syZg6WlJTY2NsyfP5/s7OxHdx4x4pmDWYc24VkAnwFvlS2L4f1E9W+Ghobs2rULGxsbXn31VYyNjfHy8uKLL754pGt08ODB7Nq1S9fN+cCaNWvIyMigWbNmmJmZ8cYbb+TafaooyrO7cuUKy5Yto0+fPlhZWTFv3jxsbW0JCQnhzJkzLFy4kNdee40Kj+nVKe6Sk5OZPn06rVu3pk6dOsTExPCf//yn0OrhHj16FBcXF5YuXYqbm1u+HdfHxwdvb+/i3QVdoHcQ+/UTMTDQDVZ5ppeBgQysVUumTZtWoCEqipI/NBqNREZGyvTp0+Xll18WMzMzGTRokKxbt05u3bql7/AKjUajkVWrVkmtWrXk7bfflosXLxZ6DFFRUWJlZSU///xzvh43IiJC6tWrJ5mZmfl63MJWsLU6IyPBwUFbiSUvm6Md4FIf2GloSF8RDh469NyldBRFKVjJycns2rVLN0mrsbEx7u7uuLm50alTJ8qXL6/vEAtVeHg4np6elCtXjoULF2JnZ1foMURHR+Pk5MQ333zDwIED8/XYb731FnZ2dkyYMCFfj1voCjy1fv+9SKVKeWrl/QpSG8QIpGH16rJixYoCD09RlGdz6dIl+f7776V3795StWpV6dGjhyxcuFBOnz6t79D05ty5czJgwACpU6eO+Pn56e153xMnTkiNGjVk3bp1+X7sCxcuiIWFhdy9ezffj13Y1OwMiqI8kUajITIyUjcK8++//8bFxQU3Nzd69uypG0hWGiUmJvLFF1/w448/4uXlhZeXl97KpcXExNCjRw/mzp2bY2xGfvH09KR8+fLMmzcv349d6AotxUZGivTvL1KxooiRUc7WnpGRdnn//trtFEXRq7t378rPP/8sI0aMkOrVq0vz5s1l8uTJsn//fl1Zv9IsMzNTFi9eLFZWVjJq1Ci5cuWKXuM5ffq01KpVq8B6yRISEsTMzExiY2ML5PiFrfAmon3gxg3tDOzHj0NCApiZQcuW2lGgJXAos6IUFxcuXNC16g4ePEinTp10c9fVr19f3+EVGTt37sTLywtLS0t8fX31Pgbh/PnzODg4MHXqVN55550COcf8+fP5888/n3t6uqKm8BOfoihFgkaj4eDBg7pkd/PmTVxdXXFzc8PJyYmqVavqO8Qi5eTJk0ycOJHTp08zf/58Xn/9db0P6b948SIODg5MmjSJsQV0eygzM5MGDRrw66+/6j3J5xdVskRRSpHExESCg4Px9/cnKCiI2rVr6x5wbt++fbGZpLUw3bp1ixkzZrBhwwbd/J9F4dnDy5cv06NHD7y9vQss6QFs3LiRhg0blpikByrxKUqJd+bMGV2r7vDhw3Tt2hU3NzfmzJmTozatklNGRgb//e9/mTNnDm+99RYnT57U1QDWt7i4OHr06MH777/P+++/X2DnERF8fHyYNWtWgZ1DH1TiU5QSJjMzk99++02X7O7du4ebmxsTJkzgtddey1HbVnmUiPDrr7/y0Ucf8dJLLxEWFkazZs30HZbOlStX6N69O++99x6enp4Feq7Q0FBSU1PzrdxZUaESn6KUALdv3yY4OFhXS7dBgwa4u7vj5+dH27ZtVRdmHkVFReHl5cX169f59ttv6dmzp75DyuHatWv06NGDUaNGMXHixAI/n4+PD15eXiXu90cNblGUYkhEOHXqlK5VFxUVRffu3XFzc8PV1ZWaNWvqO8Ri5erVq3z22WcEBAQwffp03nnnnSI3a0t8fDwODg4MGjSIqVOnFvj5Tp48Sffu3bl48SIVn2HCgeKgaP1kFUV5rIyMDPbt26dLdhkZGbi7uzN58mS6d++utweni7PU1FR8fX3x9fVl1KhRnDp1CtPHzAijTzdu3OC1117jzTffLJSkB7Bw4ULGjh1b4pIeqMSnKEXajRs3CAwMxN/fn5CQEJo0aYKbmxtbtmyhVatWeh9OX1yJCBs2bGDy5Mm0a9eO33//HVtbW32Hlatbt27h6OhInz59mDFjRqGcMz4+np9//pmYmJhCOV9hU4lPUYoQESE6OlrXqvvrr79wdHTEzc2N7777DisrK32HWOwdOnQIT08qta44AAAgAElEQVRPMjIyWLNmDfb29voO6bESEhJwcnKiZ8+ezJ49u9A+6Hz//fe8+eabJXJ+RFD3+BRF7x5M6Ozv74+/vz8GBga6GQ7s7e0xNDTUd4glwuXLl5k8eTJhYWF88cUXeHh4FOlBG3fu3MHJyYmuXbvi4+NTaEkvNTWVunXrsm/fPpo0aVIo5yxsqsWnKHpw7do1AgIC8Pf3Z8+ePbRs2RI3NzcCAgJo1qyZ6sLMR0lJSXz55ZcsXryYcePGsWzZMqpUqaLvsJ4oMTGRnj17YmdnV6hJD7QTdL/66qslNumBSnyKUihEhKioKF0X5pkzZ3B2dqZ///4sX768yDwYXZJoNBpWr17N1KlT6d69O1FRUcXigf179+7Ru3dvXnnlFb755ptCTXrZ2dn4+vqydOnSQjunPqjEpygFJDU1ld27d+u6MCtWrIi7uztffvklXbp0KRJlr0qq0NBQPD09qVSpEr/88gsdOnTQd0h5kpSUhIuLCy1btuS7774r9JZ/QEAAVapUKdL3PfODSnyKko/+/vtvAgIC2L59O2FhYbz88su4ubmxe/duGjVqpLowC9jZs2f56KOPOHr0KF999RUDBw4sNt/z5ORk3NzcaNSoEYsXL9bL/UcfHx+8vb2LzffseanBLYryArKzszly5IiuC/PSpUv06tVLN0mrubm5vkMsFe7cucOsWbNYvXo13t7eeHp6Fqvnz1JTU3Fzc6N27dqsWLGCsmXLFtzJ4uO1U8MdOwaJiWBiAq1a8efLL+M+ahTnzp2jfPnyBXf+IkC1+BTlGSUnJxMSEoK/vz8BAQGYmpri7u7O119/TadOnYpcxY+SLCsri6VLlzJz5kz69OlDdHQ01tbW+g7rmaSlpdG3b1+sra0LNulFRsLcuRAU9ODE/6zbsoWm6ensbtaM8lFR0L59wcRQRKgWn6LkwaVLl3RdmPv37+fVV1/VTdL60ksv6Tu8UikoKAhvb29q1qyJj48PrVu31ndIzyw9PZ1+/fphbGzM2rVrC+5D0+LFMHEipKbCE97yxcAAAyMjWLAACnCqI31TiU9RcqHRaPj99991XZhXr17FxcUFNzc3nJ2dMTEx0XeIpdZff/2Ft7c3Fy5cYMGCBbi5uRXLe1IZGRkMGDAAQ0NDNmzYoEt6Fy9epH79+mRmZuaaCOfMmcP58+f54Ycf8naiB0kvJSXvwVWqVCDJz8HBgaFDhzJ69OhH1l2+fJlmzZqRmJhYsF29AKIoioiIJCYmyqZNm2T48OFiaWkpLVq0kMmTJ8tvv/0mWVlZ+g6v1IuPj5exY8eKpaWlfP3115Kenl6o558zZ4707t07x7KXXnop12Xr169/4rEyMjKkb9++0rdvX8nIyMix7sKFCwJIZmbmiwf9++8ilSqJaNt5T30BUh0kE7T7RUZKZmamWFpaSn6kC3t7e1m+fPmLX9cLKrplCxSlEJw/f55Fixbh5ORErVq1+OGHH3S1G48fP87cuXPp1KlTwX8CVR4rPT2dBQsW0KxZM8qXL8+pU6f48MMPC/1xkG7duvHbb7+h0WgAbRGCzMxM/vjjjxzLzp49S7du3R57nMzMTAYNGoRGo+F///tfwQ4kmTtX2735DEyBINDuN3cugYGBmJmZFUR0eqMSn1KqZGVlER4ezqRJk2jWrBl2dnZERUXxf//3f1y5coXg4GDGjx9PvXr19B1qqScibN68mWbNmhEWFsb+/fv55ptv9DZStn379mRmZhIVFQXAvn376N69O40bN86xzNbWVjct1IcffoiNjQ3Gxsa88sorhIaGMmzYMFJTU/n444/p1KkTxsbGWFlZ4eXlleN869ato06dOlSrVo0vvvhCt3zGjBkMHToU0HaLGhgYsHr16ke3jY8nNTCQ4SKYAU2BeUDtp1znMGANaNuAgYGsWb4cDw+PHNusXLmSpk2bUrVqVRo0aPDIA+/btm2jTZs2GBsbY2trS3BwsG7dpUuX6Ny5M1WrVsXZ2ZmbN2/muJasrCxA2y06derUXLcFbc3VTp06YWpqSuvWrQkNDX3KlT1E301ORSloCQkJsn79ehkyZIhYWFhImzZt5LPPPpOIiAjRaDT6Dk/JxeHDh6Vbt27SsmVLCQkJ0Xc4Og4ODuLr6ysiIuPGjZMff/xRpkyZkmPZyJEjddv/9NNPcvPmTcnMzJR58+ZJxYoV5bXXXpPU1FTp2LGjrFmzRkRE7t27JwcPHhSRf7o6R48eLSkpKRIVFSUVKlSQEydOiIjI9OnTZciQIU/f9quvZFLZstIN5DZILEhLkFpP6eo8fr+7MwEkoWJFqV6lihw/fjxHV6e/v7+cPXtWsrOzJTQ0VIyMjOTIkSMiIhIRESHGxsayc+dO0Wg0EhcXJydPnhQRbVdngwYNJCYmRlJSUsTe3l4mTZqU41oedPE+adu4uDgxNzeXgIAA0Wg0snPnTjE3N5f4+Pg8/RxVi08pkU6fPo2vry/du3enTp06rF27li5dunD06FGOHj3KrFmz6NChQ5EuUlwaXblyhREjRuDm5saQIUM4evQojo6O+g5Lx97enn379gEQHh5O165d6dq1a45lD1c9GTp0KBYWFhgYGBAdHU1WVhZffPEFFStWpHz58pw9e5abN29SpUoVOnbsmONc06dPx8jIiNatW9O6dWv+/PPPx8aV67bHjrFRo2EKYIa2pfdBHq6xIuAO/A/YkJZGH2vrR56JdHV1xdbWFgMDA+zt7XF2diY8PByAH3/8kVGjRuHk5ESZMmWoVatWjrqfI0eOpFGjRhgZGTFw4EBdazk3j9t27dq1uLi44OLiQpkyZXBycqJdu3YEBgbm4QpVV6dSQmRmZrJ37168vb1p1KgRDg4OnDp1Ci8vL65du4a/vz/vvfdesajVWBqlpKQwc+ZMWrZsibW1NTExMYwZM6bI3Vvt1q0b+/fvJyEhgRs3btCwYUM6derEgQMHSEhIIDo6Osf9PR8fH5o2bYqRkRHr1q1Do9GQlJQEaBPE6dOnadKkCe3bt8ff3z/HuR5+HrFSpUq6/XKT67aJiVwBHv6Nz+tvvwfa7s41gEcuUxMFBQXRsWNHzM3NMTU1JTAwUNcNGRsb+8S5DV/4utB2l27atAlTU1Pda//+/Vy9ejVP16eetFWKrVu3bhEUFIS/vz87duzgpZdews3NjQ0bNtC2bdtiOcS9tMnOzsbPz48pU6bQsWNHDh8+TP369fUd1mPZ2dmRmJjIsmXL6Ny5MwDGxsbUrFmTZcuWUbNmTV384eHhfPXVV3Tr1o1q1aoRFBSEjY0Ncv8JsoYNG7J+/Xqys7PZsmULb7zxBrdu3cq/YE1MqAHEAc3uL4rN465dgauAAdDF1pZzD61LT09nwIABrFmzhtdff53y5cvTt29f3XXZ2Nhw7ty5XI6af2xsbBg2bBjLly9/rv1Vi08pNkSEEydOMG/ePLp27Ur9+vX5+eefcXR05K+//iIyMpLp06fz8ssvq6RXDBw4cAA7Ozu++eYb/Pz82LhxY5FOegBGRka0a9cOX19funbtqlvepUsXfH19c7T27t69S3JyMpcuXWLr1q34+vpy9+5d3fq1a9dy48YNypQpg6mpKUD+tnBbtWJg2bLMBRKAv4Hv8rirAbAd+NXQEINWrXKsy8jIID09HUtLS8qVK0dQUBA7d+7Urf/Pf/7DypUr2b17N9nZ2fz999+cOnUqf67pvqFDh7J9+3Z27NiBRqPRzWkZFxeXp/1V4lOKtIyMDEJCQvjwww956aWX6NWrF5cuXWLKlClcv36drVu3Mnr0aN0oOqXou3jxIm+99RZvvfUW48ePJyIigi5duug7rDyzt7cnPj4+R8xdu3YlPj5el/hEhKCgICpXrkxMTAwtWrSgYsWKObrag4ODad68OVWqVOHDDz9kw4YN+VtfdMQIppUrR22gPuAIvAHkdVrj5kBzAwMYMSLH8qpVq7Jo0SIGDhyImZkZfn5+9OnTR7e+Q4cOrFy5Ek9PT0xMTLC3t+fSpUv5ckkP2NjYsG3bNubMmYOlpSU2NjbMnz+f7OzsPO2vKrcoRU58fDyBgYH4+/uza9cumjZtipubG+7u7rRs2VK15oqpu3fvMnfuXJYtW8YHH3zAxIkTqVy5sr7Dyncigre3N+Hh4YSEhOhac3rRvz9s3aorU7YY2ACE5WVfAwPo1w82by7AAPVD3eNT9E5EOH78uK482MmTJ3F0dMTNzY3//ve/WFlZ6TtE5QVoNBpWrFjBtGnTcHZ25tixY9SqVUvfYRUIEWHSpEmEhoaye/du/SY94Oo773A+MBC79HTOAD7A+LzubGQEn3xScMHpkUp8il6kpaWxd+9e3SStZcqUwd3dnZkzZ9KtWzcMDfPaIaMUZbt378bLywtjY2O2b99Ou3bt9B1SgRERPv30U3bu3Mnu3buLRLWTjGbNeNfcnAtXr2IKvA38X152fFCrs4T+vFRXp1Jorl69SmBgINu3b2fv3r20atVK14XZtGlT1YVZgpw+fZqJEycSHR3NvHnzGDBgQIn/+U6fPp0tW7awZ88eLHN5BECv8jg7AwYG2paemp1BUZ6PiHD06FFdF+bZs2dxdnbG3d2dXr16Ua1aNX2HqOSz27dvM3PmTNauXcvHH3/MBx98UKwmhH1es2bNYsOGDezdu5fq1avrO5zcHT6srd0ZGKhNcA/X8DQy0iZEFxdt92YJbek9oBKfkq9SUlLYvXu3rguzUqVKuLu74+7uTpcuXUr8zM6lVWZmJosXL2b27NkMGDCAzz//vOgmgHw2d+5cVq9eTWhoaPGYBPfGDe0M7MePQ0ICmJlBy5ba0ZtFraVaQFTiU15YXFycbpLWffv28corr+i6MBs1aqTv8JQCJCIEBAQwceJE6tati4+PDy1atNB3WIVm/vz5LF++nNDQUPVITTGiEp/yzLKzszl8+LCuC/Py5cv06tULd3d3evbsWSRu6isF7/jx43h5eREXF4ePjw+9e/cu8ffxHrZw4UL++9//EhoaSu3aT5vzQClKVOJT8iQpKYmQkBD8/f0JCAjA3Nxc16qzs7PLdaZopWS6fv0606ZNY+vWrUydOpV333231HVhf/fdd/j6+hIaGkqdOnX0HY7yjNS7lfJYly5dYvv27fj7+/Pbb7/RsWNH3NzcmDJlyhOL0ColU1paGt988w3z58/Hw8ODU6dOlcrW/eLFi1mwYIFKesWYSnyKjkajISIiQteFee3aNVxcXBg9ejQbN27E2NhY3yEqeiAi/Pzzz0yaNIlWrVpx8OBBGjZsqO+w9GL58uXMnTuX0NBQNVlxMaa6Oku5u3fvsmPHDvz9/QkMDKRGjRq4ubnh5ubGq6++WuSmhVEKV2RkJF5eXty7dw9fX1969Oih75D0ZuXKlUybNo09e/aU2sRfUqjEVwqdO3dO16p7UCDYzc0NV1dX9SlWAbQjdadMmcKuXbuYNWsWI0aMKNUfgn766ScmT57Mnj17aNy4sb7DUV6Q6uosBbKysjhw4IAu2SUkJODq6sr48ePZunUrVapU0XeIShGRnJzM/Pnz+fbbb3nvvfeIiYmhatWq+g5Lr/z8/Jg0aRK7d+9WSa+EUImvhEpISCA4OBh/f3+Cg4OpW7cubm5urF69mnbt2lGmjJqRSvlHdnY2a9euZcqUKXTt2pU//viDunXr6jssvfvf//6Ht7e3bpYQpWRQXZ0FLT5eWyXh2DFITAQTE2jVCkaOzPcqCTExMbpW3ZEjR7C3t8fd3R1XV1f1nJHyWOHh4Xh5eVG2bFkWLlyInZ2dvkMqEjZv3sy4cePYuXMnrf41GatSvKnEV1AiI7V18YKCtF+npf2z7kFdvN69tXXx2rd/rlNkZmYSHh6uKw+WnJyse7auR48eVKpUKR8uRCmpzp8/z6RJk4iIiODLL7/k7bffVj0B923bto0xY8YQHBxM27Zt9R2Oks9U4isIBVgJ/datW7pJWnfu3EnDhg11ozDbtm1bqipnKM8nMTGROXPm8MMPP+Dp6YmXl5f6kPQQf39//vOf/xAYGMgrr7yi73CUAqDu8eW3B0kvJeXp24pot5s4Uft1LslPRDhx4oSuC/PYsWP06NEDd3d3vv76a2rUqJHPF6CUVFlZWfzwww/MmDEDFxcXjh8/rupL/ktQUBCjRo3C399fJb2STIo5Z2dnmTp16iPLt27dKlZWVpKZmVl4wfz+u0ilSiLalPZsr0qVRCIjRUQkLS1NduzYIW5ublKhQgUxMDAQQ0NDcXBwkDNnzhTe9Sglxo4dO6RFixZib28vR44c0Xc4RdKOHTvE0tJSDhw4oO9QlAJW7BOfn5+f1KtXT7Kzs3MsHzBggHh5eRVuMP36iRgYPFfiyzYwkAuvvCL9+/cXY2NjadSokRgaGsqcOXMkOTlZrl69KiNHjpS6devK7du3C/e6lGLr5MmT4uLiIra2trJly5ZH/k4Urd27d4ulpaWEh4frOxSlEBT7xJeSkiLGxsYSFhamW3b79m0xNDSUqKgoERHx9/eXNm3aSNWqVaV27doyffr0HMcIDw8XOzs7MTExkdq1a8vKlStFRMTe3l6WL1+u227lypXSuXNnERG5cOGCAP+0KK9fF3sDA1l+P5GtBOkEMgHEBKQ+yG/3l9cGsQRZ9a/kl16mjKxftEiuXbsmderUka+++ipHnBqNRpo3by5Tp06VtLQ0MTExkePHj+vWx8fHS8WKFeX69esiIrJ9+3Zp3bq1mJiYiJ2dnfz555+6bevWrSvz58+Xli1birGxsQwcOFBSU1Nf7IehFBk3b96U8ePHS7Vq1cTHx0fS09P1HVKRFRoaKpaWljneQ5SSrdgP4TIyMmLgwIGsWbNGt2zjxo00adKE1q1bA1C5cmXWrFnDnTt3CAgIYPHixWzduhWAy5cv07t3b95//31u3LhBVFQUbdq0efZAVq16ZFEE0Aq4BQwG3gYigbPAWmA8kPTQ9hUMDXk7LY2EhAQuX77Mm2++meN4ZcqUYcCAAYSEhGBoaEj//v1Zv359juu2t7enevXq/PHHH4waNYqlS5dy69Yt3n33Xfr06UN6enqO7YODg7lw4QLHjh1jVS7XoBQvGRkZLFy4kCZNmiAinDx5Ei8vLypUqKDv0Iqk8PBw3njjDTZs2EC3bt30HY5SSIp94gMYPnw4mzZtIjU1FYA1a9YwfPhw3XoHBwdatmxJmTJlaNWqFYMGDSIsLAyAdevW4ejoyKBBgyhfvjwWFhbPl/iOHXtkBGd9YCRQFngLiAWmAYaAM1ABbRLUSU2F48e5efMmQK4DV2rUqKFbP3jw4ByJz8/Pj8GDBwPaYrrvvvuurt7m8OHDMTQ05NChQ7rtP/jgA2rWrIm5uTnu7u5ERUU9+3UrRYKIsG3bNlq0aEFISAhhYWF89913VKtWTd+hFVkHDhxgwIAB+Pn5leoapKVRiUh8Xbp0wdLSkm3btnH+/HkiIyN1CQAgIiKC7t27Y2lpiYmJCUuWLNElj9jY2PyZYicx8ZFFVg/93+gxyx5u8QGQkKB7s7p69eojx7x69apufY8ePUhNTSUiIoJLly4RFRVFv379AO2UQj4+PpiamupesbGxXLlyRXcsa2tr3f8rVapEUtIj0SjFQFRUFI6OjkyZMoVvv/2WwMBAmjVrpu+wirSIiAj69u3LmjVrcHJy0nc4SiErEYkPwMPDgzVr1vDTTz/h7OyMldU/KWbw4MH06dOH2NhYEhMTee+995D7rTMbGxvOnTuX6zErV65MykOPJVy7di3HOuCf9SYmXCMfmJnRuHFjateuzaZNm3Ksys7OZvPmzbz22muAtutz4MCBrF+/Hj8/P9zc3HR1FW1sbPj000+5c+eO7pWSksKgQYPyI0qlCLh27RqjR4+mV69evPHGG/z555/07NlT32EVeYcPH6ZPnz6sXLmSXr166TscRQ9KVOLbtWsXy5cvz9HNCXDv3j3Mzc2pWLEiv//+O35+frp1Q4YMYdeuXWzcuJGsrCxu3bql6/Jr06YNW7ZsISUlhbNnz/Ljjz/q9rO0tKRWrVqsXbsWjUbDivR0ck+fz8DICFq2xMDAgAULFjB79mz8/PxITU3VvcndvXsXT09P3S6DBw/mf//7H+vWrcvRyn3nnXdYsmQJERERiAjJyckEBARw7969F41S0bPU1FTmzJlDixYtMDMz49SpU4wdO5Zy5dRjuU9z9OhRXF1dWb58Oa6urvoOR9EX/Y6tyV/29vZiamoqaWlpOZZv2rRJ6tSpI1WqVBFXV1cZN26cDBkyRLd+37590qFDB92oz1WrVomIyI0bN8TJyUmqVKkinTp1kunTp+tGdYqIBAYGSr169cTExES83ntPuv1rVGfnh0ZsngHhX6M4a4GEP7ysYkWR+Hjd8bdu3Srt2rWTSpUqiZmZmbz99tty+fLlR67b1tZWzMzMHhm5FxQUJO3atRMTExOxtraWN954Q+7evSsi2lGdISEhum2nT5+e43uiFD3Z2dmyfv16qVu3rvTv31/Onj2r75CKlaioKLGyspLNmzfrOxRFz1TJsvzUvz9s3frkMmWPY2AA/frB5s35H5dS7EVERODp6UlaWhoLFy7E3t5e3yEVK9HR0Tg5ObFo0aJHRksrpU+J6eosEj75RNtd+TyMjLT7K8pDLl++zJAhQ+jfvz9jxozh8OHDKuk9oxMnTuDs7Iyvr69KegqgEl/+at9eW3D6WQv+Vqqk3a9du4KJSyl2kpKSmDp1Km3btsXW1paYmBhGjBihZk94RjExMTg5OTFv3jw1sEvRUX9F+W3s2H+S39NmSjAw+Cfp5XF2BqVky87OZuXKlTRu3JgLFy4QFRXFzJkzqVKlir5DK3bOnDmDo6MjX3zxBUOHDtV3OEoRou7xFZTDh7Xz8QUGahPc/YfrgX/m43Nx0XZvqpaeAoSFheHp6UnFihVZuHAhr776qr5DKrbOnTtH9+7dmTZtGqNHj9Z3OEoRoxJfQbtxQ1vO7PhxSEgAMzNo2RJGjMj3GdiV4uns2bN8/PHH/PHHH3z11VcMHDhQzav4Ai5cuICDgwNTpkzh3Xff1Xc4ShGkEp+i6MmdO3eYPXs2q1atwtvbmwkTJmD0vIOjFEA7GMje3p6JEycybtw4fYejFFHqiVdFKWRZWVksW7aMzz//nD59+hAdHZ2jfJySi/h4bc/JsWPa8oAmJtCqFYwcqes5iYuLo0ePHnz44Ycq6SlPpFp8ilKIgoOD8fb2xtraGl9fX90MIspjREZq75UHBWm/Tkv7Z92De+W9e3Nj9Gg6ffgh7733Ht7e3vqJVSk2VOJTlEJw4sQJvL29OXfuHAsWLMDd3V3dx3uaxYth4kTtwLAnvE2JgQFpwL4+feh5f7oxRXkS9TiDohSgmzdvMm7cOBwcHOjZsyfR0dH06dOnSCW9nj17Mm3atEeWb9u2DWtra7Kysgo/qAdJLyXlqZWQDEQwEqFnSIh2v/scHByoWLEiVapUoVq1avTv3z/XGU+U0kclPkUpAOnp6fj4+NC0aVPKli3LyZMnmTBhQpGcEHbEiBH89NNP/Lvz56effmLIkCGFX/w6MvKfpPcsUlK0+x0+rFv03XffkZSUxOnTp7lz506OAu9K6aUSn6LkIxFhy5YtNG/enL179xIeHs6iRYuwsLDQd2iP1bdvX27fvk14eLhuWUJCAv7+/nh4eAAQEBBA27ZtMTY2xsbGhhkzZuQ4xv79++nUqROmpqbY2NiwatUqQNvq+uGHH3TbrVq1ii5dugBw8eJFDAwMcrQoHRwc+OHddyE1lVVAZ8ATMAUaAAeAVYANUB1Y/e+LSU3V3hP8F3NzcwYMGEB0dPRT4wIwMDDg+++/p2HDhlStWpWpU6dy7tw57OzsMDY2ZuDAgWRkZAAQGhpK7dq1mTNnDtWqVaNevXqsW7fuCd9xRd/UqE5FySd//PEHXl5e3Lp1i8WLFxebCU6NjIwYOHAga9asoVu3bgBs3LiRJk2a6AbfVK5cmTVr1tC8eXNdwec2bdrQt29fLl++TO/evVm2bBlvvPEGd+/eJTY29vmCyczUPvN6v/UZAYwGbgHTgbcBd+AsEAYMuP/S1bUR0RaNuHEjx2Fv3rzJ5s2badu2bZ5DCQ4O5siRI8TGxvLyyy9z4MAB1q1bh4WFBXZ2dqxfv143Bdq1a9e4efMmf//9N4cOHcLFxYV27drRuHHj5/s+KAVKtfgU5QVduXKFkSNH4uLiwqBBgzh69GixSXoPDB8+nE2bNpF6v8LQmjVrcsxr6eDgQMuWLSlTpgytWrVi0KBBhIWFAbBu3TocHR0ZNGgQ5cuXx8LCgjZt2jxfINdyTudcHxgJlAXeAmKBaYAh4AxUQJsEczAw0D76AHzwwQeYmprSunVratSoga+vb55DmTRpEsbGxjRv3pwWLVrg7OxMgwYNMDExoXfv3hw9ejTH9rNmzcLQ0BB7e3tcXV3ZuHFj3q9bKVQq8SnKc0pJSWHWrFm0bNkSKysrYmJiePfdd4vlhLBdunTB0tKSbdu2cf78eSIjI3NMbBwREUH37t2xtLTExMSEJUuWcPPmTQBiY2OxtbXNn0CSkuChrk+rh1YZPWZZ0r+PkZqqbTUCixYt4s6dO/z999+sW7cOy2eolmRl9c+ZjIyMHvk6KemfM5uZmVG5cmXd13Xr1uXKlSt5PpdSuFTiU5RnlJ2dzbp162jSpAnHjx/n8OHDfPnll5iYmOg7tBfi4eHBmjVr+Omnn3B2ds7xRj948GD69OlDbGwsiYmJvPfee7rBMDY2Npw7dy7XY1auXJmUhwapXHuoRfcgUeRYn5ycPxeTkPDE1U+K6/lOl0DyQ7FfvnyZmjVrvtAxlYKjEp9SusTHw7x5MHQouLtr/50375F7Qo9z4MAB7Ozs+Prrr/Hz82Pjxo3Ur1+/gIMuHM9X3bEAAATWSURBVB4eHuzatYvly5fn6OYEuHfvHubm5lSsWJHff/8dPz8/3bohQ4awa9cuNm7cSFZWFrdu3SIqKgqANm3asGXLFlJSUjh79iw//vijbj9LS0tq1arF2rVr0Wg0rFixgnPPOpLzcczMnrj6SXE9r+nTp5ORkUF4eDj+/v5q7r8iTCU+pXSIjIT+/aFuXZg+HdatA39/7b8zZkCdOtr1kZG57n7p0iXefvtt3nrrLcaPH09ERESOUYAlQb169ejUqRPJycn06dMnx7rvv/+eadOmUbVqVWbOnMnAgQN16+rUqUNgYCA+Pj6Ym5vTpk0b/vzzTwA8PT2pUKECVlZWDB8+nCFDhuQ47vLly5k/fz4WFhb89ddfdKpXD160q9jISFsI/gmeFtezsra2xszMjJo1azJkyBCWLFlCkyZNXuiYSsFRlVuUki+PFUAwMNC+aT40P+K9e/eYO3cuS5cu5YMPPmDixIk57uUo+Sw+Xvvh5OHSZM+qYkW4fLnQZj8JDQ1l6NChxMXFFcr5lBdX/O7CK8qzeLgCyNOI6B6Czs7OZoWhIdOmTcPJyYljx45Rq1atgo+3tKteHXr3hq1bn1qxJVcGBtp5LtWUX8oTqK5OJV8VqfJXL1ABJP399zn03Xf8+uuvrF69miFDhqjyV4Xlk0+0Le/nYWSk3V9RnkAlPiVfFanyV3Pnars3n4MhsLxBA9q1a6dbpspfFZL27bXdzZUqPdt+lSpp93voZ1YYHBwcVDdnMaMSn5Kvikz5q/h4HLZu5Yf7CXgVz1b+qowIBkFBuY72VOWvCsHYsf8kv6cV9DYw+Cfp3b83qyhPohKfkq8eLn/1wOPKX925c4eAgAAWL17M1vvTyTwof/X+++9z48YNoqKinq8KyP1k+bAIoBXa8leD0Za/ikRb+WMtMJ5/PQz9UAWQh71I+atDhw4xb948xowZw7p164iNjSU6Opr169frtn24/NXq1asZM2YMMTExeT5XiTF2LISFQb9+2gEr/+7+NDLSLu/XT7udSnpKHqnEp+S7IlH+6tixRwZHPHP5q4cqgIAqf6UX7drB5s3aUZqffw7DhoGbm/bfzz/XLt+8udC7N5XiTY3qVPLdw+WvOnToQGRkJFu2bNGtj4iIYPLkyURHR5ORkUF6erruYd98K3+VmPjIoucqf/VQBZBFixYxevTo5wrnaeWvHq4cospf5cLSEj76SN9RKCWEavEpBULv5a9MTHixIlT3PaUCyNPieh6q/JWiFCyV+JQCoffyV+np5J4+n0EeKoA8La7npcpfKUrBUYlPKRB6L39VrRqdnjYa8GlEYMSIp26myl8pSvGiSpYpJVf//i9WAaRfP+3AiUKkyl8pSsFTLT6l5FIVQBRFyYVKfErJVcwqgCiKUjhUV6dS8r3A7AyKopQ8KvEppcPhw9ranYGB2gT3cA1PIyNtQnRx0XZvqpaeopRoKvEppcuNG9oyZMePax9ONzPTPrIwYoSaykZRSgmV+BRFUZRSRQ1uURRFUUoVlfgURVGUUkUlPkVRFKVUUYlPURRFKVVU4lMURVFKFZX4FEVRlFJFJT5FURSlVFGJT1EURSlVVOJTFEVRShWV+BRFUZRSRSU+RVEUpVRRiU9RFEUpVVTiUxRFUUqV/wfGMJ9/yXOowQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hipe.buildings[1].elec.draw_wiring_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
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